Systems and methods for automated body mass index calculation

ABSTRACT

A system and method for automated body mass index is disclosed. The disclosed method operates within a system architecture including one or more computing devices, one or more servers, and one or more databases. A processor operating within the one or more servers executes one or more algorithms for detecting relevant features associated with a potential client&#39;s multimedia information. The method may include calculating feature values, such as abdomen circumference, face width, face height, cheekbone width, jaw width, and neck width, and the like as well as calculating the body mass index of the potential client using one or more regression algorithms. A baseline and updated BMI may be determined, and used for determining a baseline and updated value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/150,088, entitled “Systems and Methods for Automated BodyMass Index Calculation,” filed Apr. 20, 2015, which is herebyincorporated by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates in general to data analytics, and morespecifically to systems and methods for automated body mass indexcalculation.

BACKGROUND

Body mass index (BMI) is a wide known indicator employed in healthinsurance. Health insurance companies look at body mass index forinsurance rating purposes for the reason that BMI is a significantfactor correlated with health risk conditions, such as obesity and heartdisease. Individuals applying for an insurance plan may be classified indifferent categories depending on their respective BMI, which may affectlife insurance premiums set to the individuals.

Currently, many insurance companies require paramedical examiners tovisit potential clients for the purpose of administering one or moretests, including BMI samples. BMI samples could include acquiring bodilyfluids (e.g., blood, urine) from the potential clients. These testsimply potential delays in gathering the information required, and may besubject to human error. Although a BMI calculation requires only a fewpieces of information from potential clients, such as height and weight,and potential clients are able to provide this information through anycommunication technology available, there is still a need to validatethe information provided by the potential clients.

Thus, there is a need for providing systems methods to address these andother concerns.

SUMMARY

Systems and methods for an automated body mass index calculation aredisclosed. In one embodiment, a system architecture may includecomponents, such as one or more computing devices connected to one ormore servers via a network connection. In this embodiment, the one ormore servers include an analytical engine that coordinates multiplealgorithms for data fetching, image processing tasks, and predictiveanalytics. The aforementioned algorithms may be executed by the serverprocessor and/or the computing device processor. In one or moreembodiments, the one or more servers are in communication with adatabase so that the analytical engine has access to relevant multimediadata associated with a potential client.

In another embodiment, a method for an automated body mass indexcalculation may include a computing device that allows an agent torequest a body mass index calculation of a specific potential client. Inthis embodiment, the request is processed by a server. Further to thisembodiment, the server is in communication with a database containingrelevant multimedia information associated with a customer, and includesan analytical engine coordinating multiple algorithms. In one or moreembodiments, the analytical engine includes a data extraction module anda data processing module. In these embodiments, the data extractionmodule fetches relevant multimedia information regarding a potentialclient, and makes the relevant multimedia information available to thedata processing module. In one embodiment, the data processing moduleperforms feature detection over the multimedia information, computes oneor more feature values or feature vectors, normalizes the featurevalues, and uses the normalized feature values along with one or moreregression algorithms for calculating the body mass index associatedwith a potential client.

One embodiment of a computer-implemented method may include receivingheight and weight data of a potential customer. Upon receipt of theheight and weight data, a request may be made for an image of thepotential customer to be captured from a remote computing device, wherethe requested image includes a standard sized object positioned in theimage according to at least one reference point. An image of thepotential customer may be received, where the image is captured andtransmitted from the remote computing device, where the image includes astandard sized object positioned in the image according to the at leastone reference point. Upon receipt of the image, at least one anatomicalregion of the potential customer may be detected, a calculation of afeature value of the detected at least one anatomical region of thepotential customer in comparison to the standard sized object positionedin the image according to the at least one reference point may be made,where the calculation includes utilizing at least one image processingtechnique on the detected at least one anatomical region of thepotential customer and on the standard sized object positioned in theimage according to the at least one reference point feature value, thefeature value may be normalized, a body mass index (BMI) of thepotential customer may be predicted based on the normalized featurevalue, and the BMI may be caused to be transmitted to a computingdevice. In transmitting the BMI, the BMI may be caused to be displayedon a graphical user interface.

One embodiment of a system and computerized-method may include receivingheight and weight data of a potential customer. Upon receipt of theheight and weight data, a request may be made for a first electronicimage of the potential customer to be captured from an image capturedevice of the potential customer, where the requested image includes astandard sized object positioned in the image according to at least onereference point for the first image. An first electronic image of thepotential customer may be received via a communications network from theimage capture device of the potential customer at a first time, wherethe first image is captured and transmitted from the image capturedevice, where the first image includes a standard sized objectpositioned in the image according to the at least one reference pointfor the first image. A baseline body mass index (BMI) of the potentialcustomer may be computed as a function of the height and weight data andthe first image inclusive of the standard sized object positioned in theimage according to the least one reference point for the first image. Adetermination of a baseline underwriting value (e.g., for an insurancepolicy) may be made as a function of the computed BMI for the potentialcustomer (e.g., to be an insured under an insurance policy).

Updated height and weight data of the insured may be received at asecond time. Upon receipt of the updated height and weight data, arequest may be made for a second electronic image of the potentialcustomer to be captured from the image capture device of the potentialcustomer, where the requested image includes a standard sized objectpositioned in the image according to at least one reference point forthe second image. A second electronic image of the potential customermay be received via the communications network from the image capturedevice of the potential customer at a second time, where the secondimage is captured and transmitted from the image capture device, wherethe second image includes a standard sized object positioned in theimage according to the at least one reference point for the secondimage. The standard sized objects in the first and second images may bethe same or different standard sized objects. An updated BMI of theinsured may be computed as a function of the updated height and weightdata and the second image inclusive of the standard sized objectpositioned in the image according to the least one reference point forthe second image. A determination of an updated value (e.g.,underwriting value) may be made (e.g., for an insurance policy) as afunction of the computed updated BMI.

Numerous other aspects, features and benefits of the present disclosuremay be made apparent from the following detailed description takentogether with the drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be better understood by referring to thefollowing figures. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating the principles ofthe disclosure. In the figures, reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is an illustrative system architecture for automated body massindex calculation of potential customers, according to an exemplaryembodiment;

FIG. 2 is an illustrative computing device or server in which one ormore embodiments of the present disclosure operate, according to anexemplary embodiment;

FIG. 3 is an illustrative block diagram of a sub-system of a portion ofa system architecture pertaining to an analytical engine, according toan exemplary embodiment;

FIG. 4A is a diagram generally illustrating the front end of anillustrative system for automated body mass index, according to anexemplary embodiment;

FIG. 4B is a flow diagram generally illustrating an illustrative methodfor taking one or more pictures of an end user holding standard sizedobject, according to another embodiment;

FIG. 5 is a flow diagram generally illustrating an illustrative methodfor automated body mass index calculation, according to anotherembodiment; and

FIG. 6 is a flow diagram of an illustrative process for underwriting apotential customer for an insurance policy, according to an exemplaryembodiment.

DETAILED DESCRIPTION

The present disclosure is here described in detail with reference toembodiments illustrated in the drawings, which form a part here. Otherembodiments may be used and/or other changes may be made withoutdeparting from the spirit or scope of the present disclosure. Theillustrative embodiments described in the detailed description are notmeant to be limiting of the subject matter presented here.

As used here, the following terms may have the following definitions:

“Body mass index” refers to a measure of body fat based on height andweight, and which insurance companies may employ for insurance ratingpurposes.

“Feature” refers to a relevant piece of information that characterizesor that is correlated with the body mass index of a potential client.

“Feature detection” refers to the process of finding key points orfeatures in an image.

“Feature value or vector” refers to the quantitative representation ofone or more features resulting from one or more mathematical operations.

“Normalization” refers to adjusting feature values measured on differentranges to a notionally common scale.

“Image smoothing” refers to the process of reducing noise in an image inorder to capture important patterns.

Reference will now be made to the exemplary embodiments illustrated inthe drawings, and specific language will be used here to describe thesame. It will nevertheless be understood that no limitation of the scopeof the invention is thereby intended. Alterations and furthermodifications of the inventive features illustrated here, and additionalapplications of the principles of the inventions as illustrated here,which would occur to one skilled in the relevant art and havingpossession of this disclosure, are to be considered within the scope ofthe invention.

FIG. 1 is an illustrative system architecture 100 for performing anautomated body mass index (BMI) calculation. System architecture 100includes one or more client computing devices 102, network connection104, analytical engine 106, one or more databases 108, and one or morecomputing devices 110.

In FIG. 1, client computing devices 102 are operatively inbi-directional communication with network connection 104. Networkconnection 104 is operatively in bi-directional communication withanalytical engine 106. Analytical engine 106 may be operatively inbi-directional communication with database 108. Additionally, analyticalengine 106 is operatively in bi-directional communication with computingdevices 110.

In FIG. 1, client computing devices 102 may include smartphones, desktopcomputers, laptop computers, servers, tablets, PDAs, single computerswith multiple processors, several networked computers, specializedhardware, and the like. In one embodiment, potential clients employclient computing devices 102 for collecting personal information, suchas weight, height, pictures, and/or videos. In this embodiment, thepersonal information is sent to a company for further analysis, such asfor capturing image(s) of a potential customer, a BMI calculation. Inone or more embodiments, client computing devices 102 include one ormore built-in cameras.

In one embodiment, client computing devices 102 perform one or morefunctions of a server. In this embodiment, client computing devices 102are able to take pictures and videos, and store the pictures and videosin an internal memory. Further to this embodiment, the analytical engine106 operates within client computing devices 102. Therefore, the clientcomputing device's processor is able to execute software modules fordata fetching, image processing, and BMI calculation.

In FIG. 1, network connection 104 is implemented as any type of suitablehardware, software, and/or firmware that interconnect and otherwisecouple computing devices to allow effective communication between theaforementioned computing devices. Examples of network connection 104include intranets, local area networks (LANs), virtual private networks(VPNs), wide area networks (WANs), the Internet, and the like.

In FIG. 1, analytical engine 106 may be configured as a collection ofcomponents that interact with each other in order to accept requestsfrom agents and give responses accordingly. Analytical engine 106additionally includes programming running to serve the requests of otherprograms, the client programs. Thus, the server performs some tasks onbehalf of client programs. Examples of client programs running onanalytical engine 106 includes programs designed and built to storepotential customer data, process the potential customer data, performone or more BMI calculations based on the potential customer data, andprovide feedback to an agent through one or more computing devices 110.

Database 108 may be implemented as a relational database that storesinformation about both the data and how the data is related. In theseembodiments, database 108 is implemented as conventional databasemanagement systems (DBMS), such as, MySQL, PostgreSQL, SQLite, MicrosoftSQL Server, Microsoft Access, Oracle, SAP, dBASE, FoxPro, IBM DB2,LibreOffice Base, FileMaker Pro, MongoDb and/or any other type ofdatabase that may organize collections of data.

In one embodiment, data stored in database 108 includes potentialcustomers' data such as pictures, videos, height and weight information,and the like. The potential customers' information is used as forautomated BMI calculations, where BMI calculations include but are notlimited to calculations based on image processing and predictiveanalytics as well as calculations based on potential customerinformation regarding height and weight.

In FIG. 1, computing devices 110 may include smartphones, desktopcomputers, laptop computers, servers, tablets, PDAs, single computerwith multiple processors, several networked computers, specializedhardware, and the like. In one embodiment, computing devices 110 areused by an agent to perform duties associated with body mass indexcalculation.

In an illustrative operation, computing device 110 allows an agent torequest for a BMI calculation related to a potential customer. Upon theagent's request, analytical engine 106 retrieves data related to thepotential customer, such as one or more pictures as well as height andweight information. Next, analytical engine 106 may process the one ormore images and employ one or more algorithms for determining the BMI ofthe potential customer. The BMI may be compared with the BMI calculatedbased on the potential customer height and weight information. In thisexample, the results are presented to the agent through client computingdevice 110.

The computing code running in system architecture 100 includes programsdesigned and built to perform automated BMI calculations. The computingcode may process multiple elements simultaneously in multi-processorenvironments. Such a system configuration allows performing large work,such as heavy calculations and time consuming analysis, in a moreefficient manner than other approaches, such as manual work performed byhumans or approaches relying on a single computer. As will becomeapparent, functions and operations of system architecture 100 aresufficiently complex as to require implementation on a computer system,and cannot be performed in the human mind simply by mental steps.

In one embodiment, the aforementioned computing code is suited forprocessing multiple elements simultaneously to solve a problem inmulti-processor environments. In this embodiment, computing devices 110suitable for executing the computing code include a single computer withmultiple processors, several networked computers, specialized hardware,or any combination of the aforementioned elements. Further to thisembodiment, multi-processor environments include various architecturessuch as multi-core chips, clusters, field-programmable gate arrays(FPGAs), digital signal processing chips, and/or graphical processingunits (GPUs). To this end, the computing code is parallelized forexecution in a multi-processor environment including any number orcombination of the above listed architecture types. The instruction setssuitable for parallel execution generated from the computing code allowsmultiple threads of computing code to be executed concurrently by thevarious computing elements in the multi-processor environment.

FIG. 2 is an exemplary computing device 200 or server in which one ormore embodiments of the implementation operate, according to anembodiment. In one embodiment, computing device 200 includes bus 202,input/output (I/O) device 204, communication interface 206, memory 208,storage device 210 and central processing unit 212. In anotherembodiment, computing device 200 includes additional, fewer, different,or differently arranged components than those illustrated in FIG. 2.

In FIG. 2, bus 202 is in physical communication with I/O device 204,communication interface 206, memory 208, storage device 210, and centralprocessing unit 212. Bus 202 includes a path that permits componentswithin computing device 200 to communicate with each other. Examples ofI/O device 204 include peripherals and/or other mechanisms that mayenable an examiner or candidate to input information to computing device200, including a keyboard, computer mice, buttons, touch screens,touch-pad, voice recognition, biometric mechanisms, and the like. I/Odevice 204 also includes a mechanism that outputs information to theexaminer or candidate using computing device 200, such as, a display, amicrophone, a light emitting diode (LED), a printer, a speaker,orientation sensors, and the like. The orientation sensors include oneor more accelerometers, one or more gyroscopes, one or more compasses,and the like. The accelerometer provides a respective change of arespective angle about a respective axis. The gyroscope provides arespective rate of change of a respective angle about a respective axisand the compass provides a directional heading.

Examples of communication interface 206 include mechanisms that enablecomputing device 200 to communicate with other computing devices and/orsystems through network connections. Examples of memory 208 includerandom access memory 208 (RAM), read-only memory (ROM), flash memory,and the like. Examples of storage device 210 include magnetic and/oroptical recording medium, ferro-electric RAM (F-RAM) hard disks,solid-state drives, floppy disks, optical discs, and the like. In oneembodiment, memory 208 and storage device 210 store information andinstructions for execution by central processing unit 212. In anotherembodiment, central processing unit 212 includes a microprocessor, anapplication specific integrated circuit (ASIC), or a field programmableobject array (FPOA), and the like. In this embodiment, centralprocessing unit 212 interprets and executes instructions retrieved frommemory 208 and storage device 210.

According to some aspects of this embodiment, computing device 200 isimplemented as part of a server, client computing devices 102, computingdevices 110, or other components of system architecture 100. Examples ofthese implementations include servers, authorized computing devices,smartphones, desktop computers, laptop computers, tablet computers,PDAs, another type of processor-controlled device that receives,processes, transmits digital data, and the like. Additionally, computingdevice 200 performs certain operations that are required for the properoperation of the systems and methods described herein. Suitablecomputing devices 200 perform these operations in response to centralprocessing unit 212 executing software instructions contained in acomputer-readable medium, such as memory 208.

In one embodiment, the software instructions of the system are read intomemory 208 from another memory location, such as storage device 210, orfrom another computing device 200 (e.g., client computing devices 102,computing devices 110 and the like) via communication interface 206. Inthis embodiment, the software instructions contained within memory 208cause central processing unit 212 to perform processes that will bedescribed below in FIGS. 3-4.

FIG. 3 is an illustrative block diagram of a sub-system 300 of a portionof system architecture 100 pertaining to analytical engine 106. In oneembodiment, analytical engine 106 further includes data extractionmodule 302 and data processing module 304. Although analytical engine106 includes the listed components, it should be understood thatanalytical engine 106 can include less components, more components, ordifferent components depending on the desired analysis goals. In FIG. 3,analytical engine 106 is operatively coupled and in bi-directionalcommunication with database 108.

In one embodiment, analytical engine 106 is implemented as one or morecomputer software modules that include programmatic rules or logic foranalyzing data and calculating the body mass index (BMI) of a potentialcustomer. In this embodiment, data extraction module 302 retrieves datarelated to the potential customer from database 108. The data is thenprocessed by data processing module 304, which performs one or moreimage processing tasks and provides insight for determining the BMI ofthe potential customer. Further to this embodiment, the results derivedfrom data processing module 304 may be presented through computingdevice 110, where computing device 110 previously requested the BMIcalculation associated with the potential customer.

Data extraction module 302 is configured to retrieve data regarding apotential customer, where the data is stored in database 108. The dataincludes pictures, height and weight information, and the like. Next,data extraction module 302 feeds data processing module 304 with thedata retrieved.

In one embodiment, data processing module 304 is configured to calculatethe BMI of a potential customer based on predictive analytic techniques.The predictive analytic techniques rely on a set of relevant featuresderived from the potential customer's pictures and/or videos. In oneembodiment, the predictive analytic techniques include feeding featurevalues of a large number of people (“large set of feature values”) intoa neural network so that the neural network can learn he large set offeature values and generate a predictive model and predicting featurevalues of the potential customer with the resulting neural network viathe predictive model after it has learned the large set of featurevalues. In a particular embodiment, the predictive model is retrainedaccording to active learning that involves storing a probabilitydistribution of the large set of feature values, identifying areas inthe probability distribution where there is not much knowledge orevidence, gathering data in the identified areas, and indicating theneed to retrain the predictive model when enough data is gathered in theidentified areas. The relevant features are derived using one or moreimage processing algorithms. Additionally, data processing module 304 isable to calculate the BMI of a customer using height and weightinformation.

In an illustrative operation, given a BMI calculation request, dataextraction module 302 retrieves information related to a potentialcustomer, such as one or more pictures as well as height and weightdata. Next, data processing module 304 processes the one or morepictures, extracting one or more relevant features, and determines theBMI of the potential customer. The BMI can be compared with the BMIcalculated based on the potential customer height and weightinformation. In this example, the results derived from data processingmodule 304 may be presented to an agent through computing device 110.

A plurality of methods implemented by analytical engine 106 areperformed by one or more computing devices, such as computing device200. The methods are implemented with components of the exemplaryoperating environments of FIGS. 1-3. The steps of this illustrativeprocess are embodied in a computer readable medium containing a computerreadable code such that the steps are implemented when the computerreadable code is executed by a computing device. While the blocks in thedisclosed process are shown in a particular order, the actual order maydiffer. In some embodiments, some steps are performed in parallel.

FIG. 4A is an illustration 400 of a front end of an illustrative systemfor performing automated body mass index calculations. In oneembodiment, illustration 400 includes client computing device 402,standard sized object 404, camera 406, and end user 408 (i.e., apotential customer).

In one embodiment, end user 408 employs client computing device 402 forcollecting multimedia information that is later employed for performingan automated body mass index calculation. In this embodiment, end user408 takes/captures one or more pictures holding standard sized object404, such as an ID card, a credit card, a ruler, and the like, via agraphical user interface, implemented by a method 420 described below.Further to this embodiment, the standard sized object 404 provides asize reference that is employed in the calculation of one or morefeature values. In one or more embodiments, the feature values are usedfor making BMI predictions. In some embodiments, end user 408 takes theone or more pictures using camera 406. In these embodiments, the one ormore pictures are stored in client computing device 402 and can be sentto other components of a system architecture for performing an automatedbody mass index calculation through a network connection.

FIG. 4B is a flow diagram generally illustrating an illustrative method420 for taking/capturing the one or more pictures of end user 408holding standard sized object 404. The steps of the method areimplemented with components of the illustrative operating environmentsof FIGS. 1-3. The steps of this illustrative method are embodied in acomputer readable medium containing computer readable codes such thatthe steps are implemented when the computer readable code is executed bya computing device. In some implementations, certain steps of the methodcan be combined, performed simultaneously, or in a different order,without deviating from the objective of the method. The method starts atstep 422, where a request is received to capture an image of end user408. Method 420 then advances to step 424. At step 424, at least onereference point (e.g., a frame graphic, a rectangle graphic) isdisplayed. Method 420 then advances to step 426. At step 426, a messageis conveyed requesting end user 408 to hold standard sized object 404 upagainst an anatomical part of end user 408 (e.g., face, chest). Method420 then advances to step 428. At step 428, a message is conveyedrequesting end user 408 to align standard sized object 404 with the atleast one reference point. Method 420 then advances to step 430. At step430, when it is detected that standard sized object 404 is aligned withthe at least one reference point, an image of end user 408 holdingstandard sized object 404 is taken.

FIG. 5 is a flow diagram generally illustrating an illustrative method500 for performing an automated body mass index calculation. The stepsof the method are implemented with components of the illustrativeoperating environments of FIGS. 1-3. The steps of this illustrativemethod are embodied in a computer readable medium containing computerreadable codes such that the steps are implemented when the computerreadable code is executed by a computing device. In someimplementations, certain steps of the method can be combined, performedsimultaneously, or in a different order, without deviating from theobjective of the method.

The method starts at step 502, where one or more client computingdevices allow a potential client to provide personal information, suchas weight and height, and multimedia data, such as, pictures and videos,to a system that performs automated body mass index calculations. Insome embodiments, the multimedia data is provided to the system during alive interview with an agent. In other embodiments, the informationprovided by the potential client is stored in the system's database. Insome embodiments, the multimedia data is captured using the elements andmethods described in FIG. 5, below. Method 500 then advances to step504.

In another embodiment, the agent is in a one-on-one online interactionwith the potential client, where the agent requests one or more picturesand/or one or more videos from the potential client as well as weightand height information. The potential client takes one or more picturesand/or one or more videos using a client computing device and sends thatinformation, along with other information, using a suitable networkconnection. The potential client's information is stored in the system'sdatabase. In yet another embodiment, the disclosed method operates in aclient computing device, where the potential client's information isstored in the client computing devices' memory.

At step 504, an agent requests, through a computing device, the BMIcalculation of a potential client. Method 500 then advances to step 506.

At step 506, the analytical engine employs a data extraction module forretrieving multimedia information regarding the potential client. Method500 then advances to step 508.

At step 508, the analytical engine 106 detects one or more relevantfeatures from the multimedia data. In an example and referring to FIG.3, the feature detection step is performed by data processing module 304of analytical engine 106. In one embodiment, the data processing moduleemploys one or more computer vision or image processing algorithms forextracting relevant features from the multimedia data. Examples ofalgorithms for feature detection may include Adaboost classifier, Canyedge detector, Laplacian of Gaussian, determinant of hessian, and thelike. Optionally, the data processing module employs one or morealgorithms for image smoothing before step 508. Algorithms for imagesmoothing may include Gaussian kernel, Laplacian smoothing, low passfiltering, and the like. Method 500 then advances to step 510.

At step 510, the data processing module 304 performs one or moremathematical operations for calculating one or more feature values, suchas abdomen circumference, face width, face height, cheekbone width, jawwidth, neck width, ratio of face width to face height, distance betweeneyes, ratio of distance between of eyes to face width, total facialperimeter, ratio of width of upper face to width of lower face, eyes tonose distance, nose to mouth distance, structure of nose, and the like.In one embodiment, the calculation(s) of the feature value is assistedusing pictures and/or videos including standard sized objects, such asrulers, credit cards, ID cards, and the like. In this embodiment, thestandard sized objects provide a size reference that is employed in thecalculation of the one or more feature values. Method 500 then advancesto step 512.

At step 512, the data processing module 304 normalizes the featurevalues. In one or more embodiments, the normalization process modifiesthe feature values so that each feature contributes approximatelyproportionately to a prediction. Methods for normalization may includerescaling, standardization, scaling to unit length, and the like. Method500 then advances to step 514.

At step 514, the data processing module 304 may predict or estimate theBMI associated to a potential client by using one or more regressionalgorithms. The regression algorithm(s) may include robust regression,k-Nearest Neighbors, support vector regression, Gaussian processregression, and the like. In one or more embodiments, the aforementionedregression algorithms utilize a training face using a suitable samplesize of multimedia material. In one embodiment, the regressionalgorithms include feeding feature values of a large number of people(“large set of feature values”) into a neural network so that the neuralnetwork can learn the large set of feature values and generate apredictive model and predicting feature values of the potential customerwith the resulting neural network via the predictive model after it haslearned the large set of feature values. In a particular embodiment, thepredictive model is retrained according to active learning that involvesstoring a probability distribution of the large set of feature values,identifying areas in the probability distribution where there is notmuch knowledge or evidence, gathering data in the identified areas, andindicating the need to retrain the predictive model when enough data isgathered in the identified areas. Method 500 then advances to step 516.

At step 516, the data processing module 304 calculates the BMI using thepotential client's information regarding weight and height. In one ormore embodiments, the BMI is calculated using formula (1):

$\begin{matrix}{{B\; M\; I} = \frac{{weight}\mspace{14mu}({kg})}{{height}\mspace{14mu}(m)^{2}}} & (1)\end{matrix}$

In another embodiment, the data processing module calculates the BMIusing formula (2):

$\begin{matrix}{{B\; M\; I} = {\frac{{weight}\mspace{14mu}({lb})}{{height}\mspace{14mu}({in})^{2}}*703}} & (2)\end{matrix}$

Method 500 then advances to step 518.

At step 518, the analytical engine 106 presents the results to the agentthrough a computing device. In one embodiment, the results include (i)the BMI calculated from the potential client's entered weight andheight, (ii) the BMI calculated based on the multimedia information, and(iii) a delta value indicating an arithmetical difference between bothBMI. In some embodiments, the results provide the agent with a betterindicator regarding the potential client's actual BMI and the potentialhealth risks associated with the actual BMI.

In one or more embodiments, the analytical engine 106 operates in aclient computing device. Therefore, the client computing device'sprocessor executes one or more software modules for data fetching, imageprocessing, and BMI calculation. The BMI information is shared with thesystem's database through a network connection.

In an example and referring to FIG. 5, an agent interested incalculating the body mass index (BMI) of a potential client uses aclient computing device in order to request the BMI of a potentialclient. Analytical engine 106 operating within one or more serversemploys one or more algorithms for extracting multimedia informationassociated with the potential client. Afterwards, analytical engine 106employs computer vision and regression algorithms for calculating theBMI of the potential client which may be 23.1. This BMI may correspondto a person with normal weight. The agent compares this BMI with the BMIcalculated from information (such as weight and height) provided by thepotential client. In this example, the BMI calculated from the clients'personal information is 23. The agent realizes that both BMI areconsistent and that the potential client's weight does not represent arisk for health. This information is later employed for insurance ratingpurposes.

In another example and referring to FIG. 5, an agent and a potentialclient are conducting a video call. The agent requests one or morepictures and/or videos from the potential client as well as informationregarding weight and height. The potential client employs a clientcomputing device for taking one or more pictures and/or videos duringthe video call and delivers the information to the agent through asuitable network connection. The agent performs a BMI calculationrequest through a user interface. The agent determines that the BMIcalculated based on the multimedia information and the BMI calculatedbased on the potential client's weight and height are consistent andboth indicate that the potential client is underweight.

In yet another example and referring to FIG. 5, an agent interested incalculating the body mass index (BMI) of a potential client employs aclient computing device in order to request the BMI of a potentialclient. Analytical engine 106, operating within one or more servers,employs one or more algorithms for extracting multimedia informationassociated with the potential client. Next, analytical engine 106employs computer vision and regression algorithms for calculating theBMI of the potential client, which is 26. This BMI corresponds to anobese person. The agent compares this BMI with the BMI calculated basedon the potential client's weight and height. The BMI is 19, whichcorresponds to a person with normal weight. The agent concludes thatboth BMI are not consistent and that a further validation of thepotential client's information may be required.

By executing method 500 through the exemplary operating environmentsshown in FIGS. 1-3, big data analytics and data mining techniques can beimplemented for a more efficient and faster processing of larger datasets. In this way, efficiencies are created by providing ways toautomatically calculate and validate BMI of potential customers. Thesefeatures allow performing large work, such as heavy calculations andtime consuming analysis, in a more efficient manner than otherapproaches, such as manual work performed by humans.

One embodiment of a computer-implemented method may include receivingheight and weight data of a potential customer. Upon receipt of theheight and weight data, a request may be made for an image of thepotential customer to be captured from a remote computing device, wherethe requested image includes a standard sized object positioned in theimage according to at least one reference point. An image of thepotential customer may be received, where the image is captured andtransmitted from the remote computing device, where the image includes astandard sized object positioned in the image according to the at leastone reference point. Upon receipt of the image, at least one anatomicalregion of the potential customer may be detected, a calculation of afeature value of the detected at least one anatomical region of thepotential customer in comparison to the standard sized object positionedin the image according to the at least one reference point may be made,where the calculation includes utilizing at least one image processingtechnique on the detected at least one anatomical region of thepotential customer and on the standard sized object positioned in theimage according to the at least one reference point feature value, thefeature value may be normalized, a body mass index (BMI) of thepotential customer may be predicted based on the normalized featurevalue, and the BMI may be caused to be transmitted to a computingdevice. In transmitting the BMI, the BMI may be caused to be displayedon a graphical user interface.

In one embodiment, the standard sized object may be a credit card. Otherstandard sized objects may alternatively be utilized. The image may alsoinclude at least a partially unclothed, upper torso of the potentialcustomer. The BMI may be calculated using only the height and weightdata of the potential customer, and a delta value may be calculatedbetween (i) the BMI calculated using only the height and weight data and(ii) the BMI predicted using the normalized feature value. The deltavalue may be presented. In an embodiment, the delta value may bepresented to an agent. In predicting the BMI, a regression algorithm maybe computed, and the regression algorithm may be trained by using a setof faces associated with individuals with respective known BMIs.

In one aspect, BMI of the potential customer may be calculated usingonly the height and weight data of the potential customer, and adetermination of a category of life insurance of which the potentialcustomer qualifies may be based on the BMI calculated using only theheight and weight data. The image may be captured by a computing devicewith an integrated camera, such as a smartphone or other computingdevice. In one embodiment, the image may be a video image during areal-time video call. In an embodiment, the real-time video call may bewith an agent. Detection of the anatomical region(s) may includedetecting the anatomical region(s) utilizing at least one edge detectoron the image. A determination of a dimension of the anatomical region(s)of the potential customer may be made.

With regard to FIG. 6, a flow diagram of an illustrative process 600 forunderwriting a potential customer for an insurance policy is presented.The process 600 is a computerized-method that is to be executed by acomputing system, such as a computing system executing the analyticalengine 106 as shown in FIG. 3, and processing unit being operated by thecomputing system. The process may start at step 602. Height and weightdata of a potential customer may be received at step 604. In receivingthe height and weight data, the data may be received from the potentialcustomer entering the information via a user interface, such as awebsite, accessed by a computer, mobile device (e.g., tablet computer),or other communications device. Alternatively, the data may be receivedby an agent who receives the information in any form, such as via atelephone, or otherwise. At step 606, a first electronic image may bereceived via a communications network from an image capture device ofthe potential customer at a first time. The first image may be inclusiveof a standard sized object. The standard sized object may be a creditcard or otherwise. At step 608, a baseline body mass index (BMI) of thepotential customer may be computed as a function of (i) the height andweight data and (ii) the first electronic image of the potentialcustomer inclusive of the standard sized object. As an example, thecomputation may measure the standard sized object to determine scaling,skew, angle, and any other image information from the standard sizedobject so that anatomical regions of the potential customer can beaccurately adjusted and measured.

At step 610, a baseline underwriting value for an insurance policy maybe determined as a function of the computed BMI for the potentialcustomer to be an insured under the insurance policy. That is, thebaseline underwriting value may use the BMI of the potential customer aspart of the calculation of the baseline underwriting value, and mayinclude a variety of other factors, as well. The baseline underwritingvalue may be a first underwriting value for new or renewal potentialcustomers who have not been processed using the imaging processdescribed herein, for example.

At step 612, updated height and weight data of the insured may bereceived at a second time. The second time may be some period of time,such as one year or other time period, after the first time or into aninsurance policy that would enable the insured to receive a discount orimproved insurance plan as a result of improving his or her BMI. At step614, a second electronic image may be received via the communicationsnetwork from the image capture device of the insured at the second time.The second electronic image may be inclusive of a standard sized object.In one embodiment, the standard sized object may be the same standardsized object or same type of standard sized object (e.g., two differentcredit cards). In an alternative embodiment, the standard sized objectmay be a different standard sized object than the standard sized objectused for determining the baseline BMI. For example, a ruler may be usedfor determining the updated BMI as compared to a credit card used fordetermining the baseline BMI. At step 616, an updated BMI of the insuredmay be computed as a function of the updated height and weight data andthe second electronic image of the insured. At step 618, an updatedunderwriting value for the insurance policy may be determined as afunction of the computed updated BMI.

One embodiment of a system and computerized-method may include receivingheight and weight data of a potential customer. Upon receipt of theheight and weight data, a request may be made for a first electronicimage of the potential customer to be captured from an image capturedevice of the potential customer, where the requested image includes astandard sized object positioned in the image according to at least onereference point for the first image. An first electronic image of thepotential customer may be received via a communications network from theimage capture device of the potential customer at a first time, wherethe first image is captured and transmitted from the image capturedevice, where the first image includes a standard sized objectpositioned in the image according to the at least one reference pointfor the first image. A baseline body mass index (BMI) of the potentialcustomer may be computed as a function of the height and weight data andthe first image inclusive of the standard sized object positioned in theimage according to the least one reference point for the first image. Adetermination of a baseline underwriting value (e.g., for an insurancepolicy) may be made as a function of the computed BMI for the potentialcustomer (e.g., to be an insured under an insurance policy).

Updated height and weight data of the insured may be received at asecond time. Upon receipt of the updated height and weight data, arequest may be made for a second electronic image of the potentialcustomer to be captured from the image capture device of the potentialcustomer, where the requested image includes a standard sized objectpositioned in the image according to at least one reference point forthe second image. A second electronic image of the potential customermay be received via the communications network from the image capturedevice of the potential customer at a second time, where the secondimage is captured and transmitted from the image capture device, wherethe second image includes a standard sized object positioned in theimage according to the at least one reference point for the secondimage. The standard sized objects in the first and second images may bethe same or different standard sized objects. An updated BMI of theinsured may be computed as a function of the updated height and weightdata and the second image inclusive of the standard sized objectpositioned in the image according to the least one reference point forthe second image. A determination of an updated value (e.g.,underwriting value) may be made (e.g., for an insurance policy) as afunction of the computed updated BMI.

In computing the baseline BMI, the process 600 may include detecting atleast one anatomical region of the potential customer from the firstelectronic image. A feature value of the detected anatomical region(s)of the potential customer may be calculated, and the feature value maybe normalized. The baseline BMI may be calculated as a function of thenormalized feature value. In normalizing the feature value, the featuremay be rotated, scaled, skewed, or other mathematical function(s) may beapplied based on the standard sized object to cause the feature value tobe consistent with real-world sizing so that BMI calculations may bemore accurate. Other normalization process(es) may be utilized, as well.

Updating the updated BMI may include detecting at least one anatomicalregion of the insured from the second electronic image. The anatomicalregion may be the same anatomical region as used for determining thebaseline BMI. Alternatively, the anatomical region may be anotheranatomical region as used for determining the baseline BMI. A featurevalue of the detected at least one anatomical region of the potentialcustomer may be calculated and normalized, as previously described.Calculating the baseline BMI may include calculating the updated BMI asa function of the normalized feature value.

The process 600 may further include determining a baseline premium forthe insurance policy based on the baseline underwriting value.Determining an updated premium for the insurance policy by may includedetermining that the updated BMI is an improvement over the baselineBMI, and, in response to determining that the updated BMI is animprovement over the baseline BMI, a determination of the updatedpremium to be lower than the baseline premium may be made. If adetermination is made that the updated BMI is not an improvement overthe baseline BMI, then a determination of the updated premium to be thesame as the baseline premium may be made,

The determination of a baseline underwriting value for an insurancepolicy may include determining a baseline underwriting value for a lifeinsurance policy. In one embodiment, a notification date for notifyingthe insured to submit another image may be set. The notification datemay be within one year of setting the baseline premium. A communicationof a notification to the insured to submit another image on thenotification date may be made. In response to receiving additionalupdated height and weight data of the insured and an additional image ofthe insured, computing, by the processing unit, an additional updatedBMI of the insured may be made as a function of the additional updatedheight and weight data and the additional updated image of the insured.A determination of an additional updated underwriting value for theinsurance policy may be made as a function of the computed additionalupdated BMI. A determination of an additional updated premium for theinsurance policy based on the additional updated underwriting value maybe made. A determination may be made for a baseline premium for thepotential customer based on the baseline underwriting value, and adetermination of an updated premium for the insurance policy may be madebased on the updated underwriting value. Computing the baseline BMI andupdated BMI may further include image processing the respective firstand second electronic images using the respective standard sized objectsto determine a scale of the image to enable at least one anatomicalregion to be scaled and measured. In addition to scaling, other imageprocessing, such as determining a skew, angle, orientation, or any otherimage processing technique may be utilized.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the art,the steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc. are not intended to limit the orderof the steps; these words are simply used to guide the reader throughthe description of the methods. Although process flow diagrams maydescribe the operations as a sequential process, many of the operationsmay be performed in parallel or concurrently. In addition, the order ofthe operations may be re-arranged. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination may correspond to a return ofthe function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedhere may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to and/or incommunication with another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed here may be embodied in a processor-executable software modulewhich may reside on a computer-readable or processor-readable storagemedium. A non-transitory computer-readable or processor-readable mediaincludes both computer storage media and tangible storage media thatfacilitate transfer of a computer program from one place to another. Anon-transitory processor-readable storage media may be any availablemedia that may be accessed by a computer. By way of example, and notlimitation, such non-transitory processor-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other tangible storagemedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computeror processor. Disk and disc, as used here, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedhere may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown here but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed here.

What is claimed is:
 1. A computer-implemented method comprising: uponreceiving, by a processing unit, height and weight data of a potentialcustomer, requesting, by the processing unit, an image of the potentialcustomer to be captured from a remote computing device, the requestedimage being inclusive of a standard sized object positioned in the imageaccording to at least one reference point; receiving, by the processingunit, an image of the potential customer, wherein the image is capturedand transmitted from the remote computing device, the image beinginclusive of the standard sized object positioned in the image accordingto the least one reference point; and upon receiving the image;detecting, by the processing unit, at least one anatomical region of thepotential customer from the image, calculating, by the processing unit,a feature value of the detected at least one anatomical region of thepotential customer within the image in comparison to the standard sizedobject positioned in the image according to the at least one referencepoint, wherein the calculating comprises utilizing at least one imageprocessing technique on the detected at least one anatomical region ofthe potential customer within the image and on the standard sized objectpositioned in the image according to the at least one reference point,normalizing, by the processing unit, the feature value, executing, bythe processing unit, a predictive model to predict body mass index (BMI)of the potential customer based on the normalized feature value of theimage, the predictive model comprising a machine learning algorithmtrained in accordance with historical data corresponding to previouscustomer feature values and their respective BMI; and causing, by theprocessing unit, the BMI to be transmitted to a computing device.
 2. Themethod of claim 1, wherein receiving the image being inclusive of astandard sized object positioned in the image according to the least onereference point comprises receiving an image being inclusive of a creditcard positioned in the image according to the least one reference point.3. The method of claim 1, wherein receiving the image comprisesreceiving the image inclusive of at least a partially unclothed, uppertorso of the potential customer.
 4. The method of claim 1, furthercomprising: calculating, by the processing unit, the BMI using only theheight and weight data of the potential customer; calculating, by theprocessing unit, a delta value between (i) the BMI calculated using onlythe height and weight data and (ii) the BMI predicted using thenormalized feature value; and causing, by the processing unit, the deltavalue to be presented.
 5. The method of claim 1, wherein predicting theBMI comprises computing a regression algorithm.
 6. The method of claim5, further comprising training the regression algorithm by using a setof faces associated with individuals with respective known BMIs.
 7. Themethod of claim 1, further comprising: calculating BMI of the potentialcustomer using only the height and weight data of the potentialcustomer; and determining, by the processing unit, a category of lifeinsurance of which the potential customer qualifies based on the BMIcalculated using only the height and weight data.
 8. The method of claim1, wherein receiving the image comprises receiving an image captured bya computing device with an integrated camera.
 9. The method of claim 8,wherein receiving the image comprises receiving a video image during areal-time video call.
 10. The method of claim 1, wherein detecting theat least one anatomical region comprises detecting the at least oneanatomical region utilizing at least one edge detector on the image. 11.The method of claim 1, further comprising determining a dimension of theat least one anatomical region of the potential customer.
 12. The methodof claim 1, wherein receiving the image being inclusive of the standardsized object positioned in the image according to the least onereference point comprises: receiving, by the remote computing device, arequest to capture an image of the potential customer; displaying, bythe remote computing device, the at least one reference point;conveying, by the remote computing device, a message requesting thepotential customer to hold the standard sized object up against ananatomical part of the potential customer; conveying, by the remotecomputing device, a message requesting the potential customer to alignthe standard sized object with the at least one reference point; andcapturing, by a camera coupled to the remote computing device, an imageof the potential customer holding the standard sized object when it isdetected, by the remote computing device, that the standard sized objectis aligned with the at least one reference point.
 13. A system fordetermining body mass index (BMI) of a potential customer, the systemcomprising: a non-transitory memory configured to store data; aninput/output unit configured to bi-directionally communicate data over acommunications network; and a processing unit in communication with thenon-transitory memory and input/output unit, and configured to receiveheight and weight data of a potential customer, request, upon receivingthe height and weight of the potential customer, an image of thepotential customer to be captured from a remote computing device, therequested image being inclusive of a standard sized object positioned inthe image according to at least one reference point, receive an image ofthe potential customer, wherein the image is captured and transmittedfrom the remote computing device, the image being inclusive of thestandard sized object positioned in the image according to the least onereference point, upon receiving the image; detect at least oneanatomical region of the potential customer from the image, calculate afeature value of the detected at least one anatomical region of thepotential customer within the image in comparison to the standard sizedobject positioned in the image according to the at least one referencepoint, wherein the processing unit is further configured to calculatethe feature value by utilizing at least one image processing techniqueon the detected at least one anatomical region of the potential customerand on the standard sized object positioned in the image according tothe at least one reference point, normalize the feature value, execute apredictive model to predict body mass index (BMI) of the potentialcustomer based on the normalized feature value of the image, thepredictive model comprising a machine learning algorithm trained inaccordance with historical data corresponding to previous customerfeature values and their respective BMI, and cause the BMI to betransmitted to a computing device.
 14. The system of claim 13, whereinthe standard sized object is a credit card.
 15. The system of claim 13,wherein the image comprises an image of at least a partially unclothed,upper torso of the potential customer.
 16. The system of claim 13,wherein said processing unit is further configured to: calculate the BMIusing only the height and weight data of the potential customer;calculate a delta value between (i) the BMI calculated using only theheight and weight data and (ii) BMI predicting using the normalizedfeature value; and cause the delta value to be presented.
 17. The systemaccording to claim 13, wherein said processing unit is configured topredict the BMI by computing a regression algorithm.
 18. The systemaccording to claim 17, wherein said processing unit is furtherconfigured to train the regression algorithm by using a set of facesassociated with individuals with respective known BMIs.
 19. The systemaccording to claim 13, wherein said processing unit is furtherconfigured to calculate BMI of the potential customer using only theheight and weight data of the potential customer, and determine acategory of life insurance of which the potential customer qualifiesbased on the BMI calculated using only the height and weight data. 20.The system according to claim 13, wherein the image is captured by acomputing device with an integrated camera.
 21. The system according toclaim 20, wherein said processing unit is configured to receive theimage as a video image during a real-time video call.
 22. The systemaccording to claim 13, wherein said processing unit, in detecting the atleast one anatomical region, is configured to detect the at least oneanatomical region utilizing at least one edge detector on the image. 23.The system according to claim 13, wherein said processing unit isfurther configured to determine a dimension of the at least oneanatomical region of the potential customer.
 24. The system of claim 13,wherein the remote computing device is configured to receive a requestto capture an image of the potential customer, display the at least onereference point to the potential customer, convey a message requestingthe potential customer to hold the standard sized object up against ananatomical part of the potential customer, convey a message requestingthe potential customer to align the standard sized object with the atleast one reference point, and capture, by a camera coupled to theremote computing device, an image of the potential customer holding thestandard sized object when it is detected, by the remote computingdevice, that the standard sized object is aligned with the at least onereference point.